Weaviate vs Milvus
Weaviate
Enterprise-ready, distributed vector database with GraphQL API, advanced filtering, and multi-modal search capabilities.
Enterprises building AI search, RAG systems, and recommendation engines who need managed infrastructure and native LLM integrations; teams with smaller-to-medium scale requirements (< 2B vectors).
Milvus
High-performance open-source vector database optimized for massive-scale similarity search and cost-efficient deployment.
ML teams and research organizations managing billion-scale vector collections, AI labs prioritizing cost efficiency and control, and companies with strong DevOps/Kubernetes expertise.
Short Answer
Weaviate is a cloud-native vector database optimized for AI-powered search with built-in generative AI integrations, while Milvus is a high-performance open-source vector database designed for massive-scale similarity search with lower infrastructure costs. Weaviate excels for developers seeking managed solutions, whereas Milvus suits teams needing extreme scalability and cost control.
Our Verdict
AI-assistedChoose Weaviate if you need rapid development with managed infrastructure, native generative AI pipelines, and hybrid search capabilities—ideal for enterprises prioritizing time-to-market and developer experience. Choose Milvus if you require extreme query throughput (500K+ QPS), massive scale (4B+ vectors), cost optimization through open-source deployment, and have in-house DevOps expertise for orchestration.
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Choose Weaviate if
Enterprises building AI search, RAG systems, and recommendation engines who need managed infrastructure and native LLM integrations; teams with smaller-to-medium scale requirements (< 2B vectors).
Choose Milvus if
ML teams and research organizations managing billion-scale vector collections, AI labs prioritizing cost efficiency and control, and companies with strong DevOps/Kubernetes expertise.
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Key Differences at a Glance
Key Facts & Figures
| Metric | Weaviate | Milvus | Diff |
|---|---|---|---|
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | — | — |
| Query Latency (p99)(milliseconds) | 50-150ms | — | — |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | — | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | — | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | 500,000 QPS | -80% |
| Maximum Collection Size(billion vectors) | 2 billion vectors | 4+ billion vectors | -50% |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | 30+ minutes (Docker/K8s) | -75% |
| GitHub Community Stars(stars) | 13,000+ stars | 31,000+ stars | -58% |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | 0 (external required) | — |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | — | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — | — |
| Built-in LLM Integrations(count) | 15+ providers | — | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — | — |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | — | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | — | — |
| Average Query Latency(milliseconds) | 50-150ms | — | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | — | — |
| GitHub Stars | ~9,500 stars (as of 2026) | 25,600 | -63% |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — | — |
| Setup Time (First Query)(minutes) | 30-60 minutes | — | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | — | — |
| Monthly Cost (1M vectors, 1K queries/day)(USD) | $20-150 (infrastructure dependent) | $20-150 (infrastructure dependent) | — |
| Average Query Latency (p50)(milliseconds) | 15-80ms | 15-80ms | — |
| Setup Time (production-ready)(hours) | 4-8 hours | 4-8 hours | — |
| Native Integration Count(frameworks) | 40+ (includes Spark, Kafka, Airflow) | 40+ (includes Spark, Kafka, Airflow) | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Weaviate
Cloud-managed (SaaS) or self-hosted🏆
Milvus
Self-hosted open-source only
Weaviate
50,000-100,000 QPS
Milvus
500,000+ QPS at scale🏆
Weaviate
Native integration with 20+ LLMs🏆
Milvus
Requires external orchestration
Weaviate
Up to 2 billion vectors
Milvus
Up to 4+ billion vectors🏆
Weaviate
5-10 minutes for cloud, Helm for K8s🏆
Milvus
Docker/K8s required, 30+ min setup
Weaviate
13,000+ stars
Milvus
31,000+ stars🏆
Weaviate
Native BM25 hybrid search🏆
Milvus
Third-party integration required
Full Comparison
| Attribute | Weaviate |  Milvus |
|---|---|---|
| Free Tier Vector Limit(vectors) | Unlimited (self-hosted) | — |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — |
| Time to First Query(minutes) | 30-45 minutes (self-hosted) | — |
| Maximum Vector Dimensions(dimensions) | Unlimited | — |
| Query Latency (p99)(milliseconds) | 50-150ms | — |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | 500,000 QPS |
| GPU Acceleration Support | Limited (planning phase) | Full CUDA/GPU support |
Show 4 more attributesQuery Latency (95th percentile)(milliseconds) 100-500 ms — Throughput (vectors/second insert)(vectors/sec) 5,000-10,000 — Average Query Latency(milliseconds) 50-150ms — Average Query Latency (p50)(milliseconds) 15-80ms — | ||
| Uptime SLA(percent) | Not guaranteed (self-hosted) | — |
| Uptime SLA Guarantee(percent) | Self-managed (no SLA) | — |
| Native Hybrid Search Support(null) | BM25 keyword + vector | — |
| Built-in Hybrid Search Support | Native BM25 + vector search | Requires external tools |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | 0 (external required) |
| Hybrid Search Support (BM25 + Vector) | Yes | — |
| Multi-tenancy Support | Native with isolation | — |
Show 2 more attributesQuery Filtering Support Advanced GraphQL + WHERE clauses with boolean logic — Multi-Modal Search Text, image, audio, video — | ||
| Deployment Model | Cloud-managed SaaS + Self-hosted Docker/Kubernetes | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Built-in LLM Integrations(count) | 15+ providers | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — |
| Licensing Cost(USD) | $0-5000+/month (SaaS) | $0 (open-source) |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | 4+ billion vectors |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | — |
| Maximum Vectors Supported(billions) | Unlimited (hardware-constrained) | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| Setup Time (First Query)(minutes) | 30-60 minutes | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | 30+ minutes (Docker/K8s) |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | — |
| Setup Time (production-ready)(hours) | 4-8 hours | — |
| GitHub Community Stars(stars) | 13,000+ stars | 31,000+ stars |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — |
| Supported Deployment Modes | Docker, Kubernetes, Cloud (AWS/GCP/Azure) | — |
| Minimum Setup Infrastructure | Docker/Kubernetes cluster (4GB+ RAM minimum) | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — |
| Monthly Cost (1M vectors, 1K queries/day)(USD) | $20-150 (infrastructure dependent) | — |
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
| GitHub Stars | ~9,500 stars (as of 2026) | 25,600 |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — |
| Kubernetes Support | Native Kubernetes-ready Helm charts | — |
| LangChain Integration Maturity | Supported but secondary to GraphQL API | — |
| Native Integration Count(frameworks) | 40+ (includes Spark, Kafka, Airflow) | — |
| Data Export Capability(text) | Full; supports Parquet, Arrow, SQL dumps, zero egress cost | — |
MilvusShow 4 more attributes
Show 2 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Weaviate
Pros
- Native integration with OpenAI, Cohere, Hugging Face, and 20+ LLM providers for RAG pipelines
- Built-in BM25 keyword search merged with vector similarity in single query
- Managed cloud version requires zero infrastructure management; pricing scales with usage
- GraphQL API with 45+ filter operators for complex queries on vector metadata
- Reranking module (RRF) built-in for improved search relevance without external tools
Cons
- Query throughput maxes at 100K QPS, limiting ultra-large-scale batch operations
- Cloud pricing can reach $5,000+/month for high-volume production workloads
- Limited to ~2 billion vectors per cluster before sharding complexity increases significantly
Milvus
Pros
- Extreme query throughput: 500K+ QPS at scale with optimized indexing (HNSW, IVF-SQ8)
- Completely open-source (Apache 2.0) with zero licensing costs; self-hosted deployment keeps data private
- Scales to 4+ billion vectors across distributed clusters with built-in partitioning
- Multi-language support: Python, Java, Node.js, Go SDKs with consistent API design
- GPU acceleration support (NVIDIA CUDA) for 10-50x faster vector operations on large datasets
Cons
- No native generative AI integration; requires external frameworks (LangChain, LlamaIndex) for RAG
- Setup and maintenance demand DevOps expertise; Kubernetes deployment required for HA
- Lacks hybrid search (keyword+vector) natively; keyword filtering requires separate Elasticsearch/Postgres
Frequently Asked Questions
Use Weaviate if you want out-of-the-box RAG with native LLM integrations (OpenAI, Cohere, etc.) and minimal DevOps overhead. Use Milvus if you have 500M+ vectors, need extreme query speed (500K+ QPS), and have the engineering resources to integrate external LLM frameworks like LangChain. Weaviate prioritizes developer experience; Milvus prioritizes scale and cost.
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